Artificial Intelligence Adoption in HIV Public Health as a Fourth Industrial Revolution Strategy: Evidence, Economics and Governance Imperatives From Zambia

Abstract

The Fourth Industrial Revolution (4IR) offers low- and middle-income countries new ways to transform public-health systems, yet artificial intelligence (AI) adoption in African health has been marked by a disconnect between technological capability and institutional readiness. This review examines the business, economic and governance dimensions of AI adoption in HIV programmes, using Zambia’s national HIV electronic-health-record system as its anchoring case. Its objective is to synthesise the evidence on 4IR and health-system transformation in resource-constrained settings and to clarify the conditions under which AI adoption becomes trustworthy, equitable and sustainable. The literature was reviewed thematically across global, regional and Zambian perspectives, integrating peer-reviewed and institutional sources with empirical evidence from a multi-facility AI research programme covering 246,053 patients across six Lusaka public-health facilities. The review finds that the principal constraints on trustworthy AI are not algorithmic but structural: composite data-quality scores fall below the threshold required for reliable model training, key sociodemographic fields are largely absent from the record, and governance frameworks for responsible deployment remain underdeveloped. Interpretable models are generally preferable to complex architectures in fragmented-data settings, and data governance emerges as both the highest-value economic investment and an equity imperative, since models trained on incomplete data may act on administrative missingness rather than clinical need. The review concludes that AI adoption in public health is primarily an economic and governance challenge rather than a technological one, and that sustainable progress depends as much on investment in data governance, workforce development and regulation as on algorithmic development. It identifies specific knowledge gaps in costing, prospective evaluation and operational governance that define an agenda for future empirical research.